Question 824 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is performing hyperparameter optimization for a gradient boosting model using Amazon SageMaker Automatic Model Tuning. The objective metric is 'validation:logloss'. Which TWO strategies can help the tuning job converge faster? (Choose TWO.)
⚠ Common exam trap
Watch out — candidates often confuse 'increasing resources' (Option C) with improving convergence speed, but resource limits only affect individual training job speed, not the efficiency of the hyperparameter search itself.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Bayesian optimization strategy
Bayesian optimization is a hyperparameter tuning strategy that builds a probabilistic model of the objective function and uses it to select the most promising hyperparameter combinations to evaluate next. By focusing on regions of the hyperparameter space that are likely to yield better validation:logloss, it converges to an optimal configuration in fewer training jobs compared to uninformed search methods, thus speeding up the tuning process.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Bayesian optimization strategy
Why this is correct
Bayesian optimization intelligently selects hyperparameters to converge faster.
- ✗
Increase the number of tuning jobs
Why it's wrong here
More jobs may take longer overall.
- ✗
Increase the resource limits for each training job
Why it's wrong here
Resource limits do not affect tuning speed.
- ✗
Use random search strategy
Why it's wrong here
Random search is less efficient than Bayesian optimization.
- ✓
Use early stopping based on the objective metric
Why this is correct
Early stopping stops underperforming trials early, saving time.
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Last reviewed: Jul 4, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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